Scalable and Real-Time Sentiment Analysis of Twitter Data

In this paper, we present a system for scalable and real-time sentiment analysis of Twitter data. The proposed system relies on feature extraction from tweets, using both morphological features and semantic information. For the sentiment analysis task, we adopt a supervised learning approach, where we train various classifiers based on the extracted features. Finally, we present the design and implementation of a real-time system architecture in Storm, which contains the feature extraction and classification tasks, and scales well with respect to input data size and data arrival rate. By means of an experimental evaluation, we demonstrate the merits of the proposed system, both in terms of classification accuracy as well as scalability and performance.